The present disclosure relates to analysis of data, and more particularly to automated techniques for enabling analysis of data stored in multiple data sources.
The field of data analysis has traditionally been restricted to those with technical capabilities. The data analyst has to have technical knowhow as to how to retrieve the data to be analyzed from its storage location and program the analysis techniques. The problem is further aggravated if the data to be analyzed is stored across multiple data sources. For example, if the data to be analyzed is stored in multiple database systems, the data analyst has to know how to query and manipulate the data from the databases using a data management language such as a Structured Query Language (SQL). Additionally, the data analyst has to be well versed in writing complex queries (e.g., SQL queries) for performing the desired analysis on the retrieved data.
In recent times, data analysis is more and more being performed by business users who have very little to no technical skills. Various graphical user interface (GUI) based tools such as dashboards are being provided to enable these non-technical business users to perform data analysis. While these tools can be used by the business users, the data analysis logic provided by the tool is typically hardcoded into the tool by the engineering team supporting the tool. If a business user wants to change the analysis being performed by the tool, the change information has to be conveyed to the engineering team, which then has to change the code for the tool per the new requirements. A new version of the tool incorporating the code changes is then released and published to the business user for use. This however requires a lot of turnaround time and severely limits the flexibility of the analysis that can be performed by the business user.